Pathak et al [1]} proposed a new version of GWO for solving steganalysis using feature selection called levy flight-based grey wolf optimization (LFGWO). LFGWO overcomes the limitations of GWO and aims to seek the most prominent features in feature space. The fitness function includes one of the decision tree classifiers, called random forest. The main advantage of LFGWO compared with the relevant literature of evolutionary algorithms is its better convergence precision. They also examined 5 different classifiers - SVM, LDA, RF, KNN and ZeroR - to analyze the performance of image steganalysis over selected features. It is worth mentioning that the LDA here is used as a classifier. LDA also is used as a feature reduction algorithm [2]}. The LFGWO extracted 84 out of 686 and 89 out of 1000 features from SPAM and AlexNet extracted features, respectively. Although LFGWO obtained better results than IFAB[3]} and and advanced IFAB, called IFAB-KNN [4]}, IFAB and IFAB-KNN extracted less features, i.e. 80 out of 686 for SPAM. The results reveal that the 84 selected features were more relevant than those 80 features, particularly for SPAM.
